This is how a chronicle report looks
This is a showcase of the outputs possible with the {chronicle} R package. For a complete how-to, please refer to the package’s github page.
Barplots
Simple bar plot
Bars broken by other group
Bars sorted by value
Bars sorted by value and broken by another column
Horizontally
Boxplots
Simple box plot
Box plot by groups
Box plot with jitter
Code
This is an empty canvas for you to include any code you want.
This is some code:
data.table(iris)[, .N, Species]It can also be evaluated!
library(data.table)
data.table(iris)[, .N, Species]## Species N
## 1: setosa 50
## 2: versicolor 50
## 3: virginica 50Densities
Basic density
Density by group
Faceted densities
dygraphs
Simple dygraph (in static outputs it will be replaced by line plots)
Dygraph by groups
Histograms
Basic histogram
Faceted histogram by groups
Line plots
Simple line plot
Line plot with trend
Line plot with linear trend
Faceted line plot
Rain cloud plots
Simple rain cloud
Rain cloud by group
Larger denisty kernel
No boxplot, just the median
With the mean instead of the boxplot
Scatter plots
Simple scatter plot
Scatter plot with groups
Faceted scatter plot with trend
Tables
kable
| Sepal.Length | Sepal.Width | Petal.Length | Petal.Width | Species |
|---|---|---|---|---|
| 5.1 | 3.5 | 1.4 | 0.2 | setosa |
| 4.9 | 3.0 | 1.4 | 0.2 | setosa |
| 4.7 | 3.2 | 1.3 | 0.2 | setosa |
| 4.6 | 3.1 | 1.5 | 0.2 | setosa |
| 5.0 | 3.6 | 1.4 | 0.2 | setosa |
| 5.4 | 3.9 | 1.7 | 0.4 | setosa |
DT
Violins
Simple violin plot
Violin plot by group
sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] chronicle_0.2.5 data.table_1.13.6 magrittr_2.0.1 rlang_0.4.10
##
## loaded via a namespace (and not attached):
## [1] zoo_1.8-8 tidyselect_1.1.0 xfun_0.20 purrr_0.3.4 splines_4.0.3 lattice_0.20-41 colorspace_2.0-0
## [8] vctrs_0.3.6 generics_0.1.0 htmltools_0.5.0 viridisLite_0.3.0 yaml_2.2.1 mgcv_1.8-33 utf8_1.1.4
## [15] plotly_4.9.3 pillar_1.4.7 glue_1.4.2 withr_2.3.0 lifecycle_0.2.0 stringr_1.4.0 tictoc_1.0
## [22] munsell_0.5.0 gtable_0.3.0 htmlwidgets_1.5.3 evaluate_0.14 labeling_0.4.2 knitr_1.31 rmdformats_1.0.1
## [29] crosstalk_1.1.0.1 fansi_0.4.1 highr_0.8 xts_0.12.1 readr_1.4.0 scales_1.1.1 DT_0.17
## [36] jsonlite_1.7.2 gridExtra_2.3 ggplot2_3.3.3 hms_0.5.3 digest_0.6.27 stringi_1.5.3 bookdown_0.21
## [43] dplyr_1.0.2 grid_4.0.3 cli_2.2.0 tools_4.0.3 lazyeval_0.2.2 tibble_3.0.4 crayon_1.3.4
## [50] tidyr_1.1.2 pkgconfig_2.0.3 ellipsis_0.3.1 Matrix_1.2-18 dygraphs_1.1.1.6 assertthat_0.2.1 rmarkdown_2.6
## [57] httr_1.4.2 rstudioapi_0.13 viridis_0.5.1 R6_2.5.0 nlme_3.1-151 compiler_4.0.3report_columns()
chronicle also includes a function called report_columns(), that will create an entire chronicle report for a single dataset. It includes a comprehensive summary of the data through the skimr::skim() function, along with one plot for each column present in the data: bar plots for categorical variables and rain cloud plots for numerical variables. This gives you an immediate view of a dataset with a single line of code!
report_columns(dt = palmerpenguins::penguins,
by_column = 'species')